Plot Data
library(ggplot2)
# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=siRNA)) +
geom_point(aes(colour=siRNA, shape=Experiment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=siRNA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)
cairo_pdf("Figure5G.pdf", width = 5, height = 4, family = "Arial")
ggplot(dataset, aes(x=Olaparib, y=NormCounts2)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=TRUE,
aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
#facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
ylab(label = "Normalized Counts") +
scale_color_manual(values=c("#000000","#FF0000")) +
guides(linetype = guide_legend(override.aes= list(color = "#555555")))
dev.off()
## quartz_off_screen
## 2
Models
library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)
Linear formula
fit1 <- lm(Counts ~ Experiment + Olaparib*siRNA*genotype, data = dataset)
print(summary(fit1))
##
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * siRNA * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1060.56 -209.39 11.22 252.27 1391.68
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2274.08 195.20 11.650 < 2e-16 ***
## Experimentexp2 -68.56 152.59 -0.449 0.65462
## Experimentexp3 1714.44 152.59 11.236 < 2e-16 ***
## Experimentexp4 -523.62 152.59 -3.432 0.00102 **
## Experimentexp5 -453.81 152.59 -2.974 0.00406 **
## Olaparib -331.87 75.12 -4.418 3.67e-05 ***
## siRNAsiBRCA1 174.68 239.95 0.728 0.46913
## genotypeYFP-ALC1 -141.46 239.95 -0.590 0.55746
## Olaparib:siRNAsiBRCA1 -169.46 106.23 -1.595 0.11531
## Olaparib:genotypeYFP-ALC1 83.60 106.23 0.787 0.43406
## siRNAsiBRCA1:genotypeYFP-ALC1 -809.17 339.35 -2.384 0.01990 *
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 224.71 150.23 1.496 0.13935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 431.6 on 68 degrees of freedom
## Multiple R-squared: 0.8508, Adjusted R-squared: 0.8267
## F-statistic: 35.26 on 11 and 68 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit1))
## AIC: 1210.822
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit2))
##
## Call:
## lm(formula = NormCounts ~ Olaparib * siRNA * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14430 -0.05874 -0.01280 0.05396 0.27693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.35130 0.03274 41.277 < 2e-16 ***
## Olaparib -0.18909 0.01449 -13.047 < 2e-16 ***
## siRNAsiBRCA1 0.22874 0.04630 4.941 4.90e-06 ***
## genotypeYFP-ALC1 -0.09095 0.04630 -1.964 0.053343 .
## Olaparib:siRNAsiBRCA1 -0.12312 0.02050 -6.007 7.04e-08 ***
## Olaparib:genotypeYFP-ALC1 0.04895 0.02050 2.388 0.019548 *
## siRNAsiBRCA1:genotypeYFP-ALC1 -0.19542 0.06547 -2.985 0.003875 **
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 0.10519 0.02899 3.629 0.000529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08327 on 72 degrees of freedom
## Multiple R-squared: 0.9216, Adjusted R-squared: 0.914
## F-statistic: 121 on 7 and 72 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit2))
## AIC: -161.1043
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit3))
##
## Call:
## lm(formula = NormCounts2 ~ Olaparib * siRNA * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109758 -0.045560 -0.009451 0.041690 0.184443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.040465 0.024147 43.088 < 2e-16
## Olaparib -0.145595 0.010690 -13.619 < 2e-16
## siRNAsiBRCA1 0.011865 0.034149 0.347 0.7293
## genotypeYFP-ALC1 0.004457 0.034149 0.131 0.8965
## Olaparib:siRNAsiBRCA1 -0.062342 0.015119 -4.124 9.87e-05
## Olaparib:genotypeYFP-ALC1 0.029411 0.015119 1.945 0.0556
## siRNAsiBRCA1:genotypeYFP-ALC1 -0.034710 0.048295 -0.719 0.4746
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 0.053641 0.021381 2.509 0.0144
##
## (Intercept) ***
## Olaparib ***
## siRNAsiBRCA1
## genotypeYFP-ALC1
## Olaparib:siRNAsiBRCA1 ***
## Olaparib:genotypeYFP-ALC1 .
## siRNAsiBRCA1:genotypeYFP-ALC1
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06142 on 72 degrees of freedom
## Multiple R-squared: 0.9256, Adjusted R-squared: 0.9183
## F-statistic: 127.9 on 7 and 72 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit3))
## AIC: -209.7982
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * siRNA * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 1103.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.32607 -0.48408 0.07435 0.38216 2.72754
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 944134 971.7
## Residual 77744 278.8
## Number of obs: 80, groups: UID, 20
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2407.76 448.16 17.37 5.373
## Olaparib -331.87 48.53 56.00 -6.838
## siRNAsiBRCA1 174.68 633.79 17.37 0.276
## genotypeYFP-ALC1 -141.46 633.79 17.37 -0.223
## Olaparib:siRNAsiBRCA1 -169.46 68.63 56.00 -2.469
## Olaparib:genotypeYFP-ALC1 83.60 68.63 56.00 1.218
## siRNAsiBRCA1:genotypeYFP-ALC1 -809.17 896.31 17.37 -0.903
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 224.71 97.06 56.00 2.315
## Pr(>|t|)
## (Intercept) 4.71e-05 ***
## Olaparib 6.40e-09 ***
## siRNAsiBRCA1 0.7861
## genotypeYFP-ALC1 0.8260
## Olaparib:siRNAsiBRCA1 0.0166 *
## Olaparib:genotypeYFP-ALC1 0.2283
## siRNAsiBRCA1:genotypeYFP-ALC1 0.3790
## Olaparib:siRNAsiBRCA1:genotypeYFP-ALC1 0.0243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Olaprb sRNAsBRCA1 gYFP-A Ol:RNABRCA1 O:YFP- sRNABRCA1:
## Olaparib -0.201
## siRNAsBRCA1 -0.707 0.142
## gntYFP-ALC1 -0.707 0.142 0.500
## Ol:RNABRCA1 0.142 -0.707 -0.201 -0.101
## Ol:YFP-ALC1 0.142 -0.707 -0.101 -0.201 0.500
## sRNABRCA1:Y 0.500 -0.101 -0.707 -0.707 0.142 0.142
## O:RNABRCA1: -0.101 0.500 0.142 0.142 -0.707 -0.707 -0.201
cat("AIC: ", AIC(fit4))
## AIC: 1123.559
simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula
fit5 <- lm(Counts ~ Experiment + poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit5))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * siRNA *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -946.66 -275.06 28.98 227.19 1267.54
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1657.51 137.15 12.085
## Experimentexp2 -68.56 153.34 -0.447
## Experimentexp3 1714.44 153.34 11.181
## Experimentexp4 -523.62 153.34 -3.415
## Experimentexp5 -453.81 153.34 -2.960
## poly(Olaparib, 2)1 -3813.51 867.41 -4.396
## poly(Olaparib, 2)2 -954.25 867.41 -1.100
## siRNAsiBRCA1 -140.15 137.15 -1.022
## genotypeYFP-ALC1 13.85 137.15 0.101
## poly(Olaparib, 2)1:siRNAsiBRCA1 -1947.25 1226.70 -1.587
## poly(Olaparib, 2)2:siRNAsiBRCA1 158.88 1226.70 0.130
## poly(Olaparib, 2)1:genotypeYFP-ALC1 960.61 1226.70 0.783
## poly(Olaparib, 2)2:genotypeYFP-ALC1 18.02 1226.70 0.015
## siRNAsiBRCA1:genotypeYFP-ALC1 -391.70 193.96 -2.020
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 2582.11 1734.82 1.488
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 476.17 1734.82 0.274
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Experimentexp2 0.65629
## Experimentexp3 < 2e-16 ***
## Experimentexp4 0.00111 **
## Experimentexp5 0.00432 **
## poly(Olaparib, 2)1 4.24e-05 ***
## poly(Olaparib, 2)2 0.27540
## siRNAsiBRCA1 0.31069
## genotypeYFP-ALC1 0.91988
## poly(Olaparib, 2)1:siRNAsiBRCA1 0.11735
## poly(Olaparib, 2)2:siRNAsiBRCA1 0.89735
## poly(Olaparib, 2)1:genotypeYFP-ALC1 0.43647
## poly(Olaparib, 2)2:genotypeYFP-ALC1 0.98833
## siRNAsiBRCA1:genotypeYFP-ALC1 0.04763 *
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.14156
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.78460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 433.7 on 64 degrees of freedom
## Multiple R-squared: 0.8582, Adjusted R-squared: 0.825
## F-statistic: 25.83 on 15 and 64 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit5))
## AIC: 1214.757
simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit6))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * siRNA * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.197464 -0.031413 0.000256 0.029071 0.197247
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 1.392e-02 71.815
## poly(Olaparib, 2)1 -2.173e+00 1.245e-01 -17.446
## poly(Olaparib, 2)2 -5.550e-01 1.245e-01 -4.456
## siRNAsiBRCA1 2.245e-16 1.969e-02 0.000
## genotypeYFP-ALC1 3.394e-16 1.969e-02 0.000
## poly(Olaparib, 2)1:siRNAsiBRCA1 -1.415e+00 1.761e-01 -8.032
## poly(Olaparib, 2)2:siRNAsiBRCA1 -4.597e-02 1.761e-01 -0.261
## poly(Olaparib, 2)1:genotypeYFP-ALC1 5.625e-01 1.761e-01 3.194
## poly(Olaparib, 2)2:genotypeYFP-ALC1 6.544e-02 1.761e-01 0.372
## siRNAsiBRCA1:genotypeYFP-ALC1 -4.411e-16 2.785e-02 0.000
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 1.209e+00 2.491e-01 4.852
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 3.531e-01 2.491e-01 1.417
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 < 2e-16 ***
## poly(Olaparib, 2)2 3.20e-05 ***
## siRNAsiBRCA1 1.00000
## genotypeYFP-ALC1 1.00000
## poly(Olaparib, 2)1:siRNAsiBRCA1 1.91e-11 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1 0.79491
## poly(Olaparib, 2)1:genotypeYFP-ALC1 0.00213 **
## poly(Olaparib, 2)2:genotypeYFP-ALC1 0.71141
## siRNAsiBRCA1:genotypeYFP-ALC1 1.00000
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 7.45e-06 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.16094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06227 on 68 degrees of freedom
## Multiple R-squared: 0.9586, Adjusted R-squared: 0.9519
## F-statistic: 143.2 on 11 and 68 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit6))
## AIC: -204.1673
simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit7))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * siRNA * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.131514 -0.023050 0.000168 0.022818 0.131370
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.76997 0.01013 76.015
## poly(Olaparib, 2)1 -1.67303 0.09060 -18.466
## poly(Olaparib, 2)2 -0.42734 0.09060 -4.717
## siRNAsiBRCA1 -0.10396 0.01432 -7.257
## genotypeYFP-ALC1 0.05910 0.01432 4.125
## poly(Olaparib, 2)1:siRNAsiBRCA1 -0.71637 0.12813 -5.591
## poly(Olaparib, 2)2:siRNAsiBRCA1 0.02708 0.12813 0.211
## poly(Olaparib, 2)1:genotypeYFP-ALC1 0.33796 0.12813 2.638
## poly(Olaparib, 2)2:genotypeYFP-ALC1 0.02145 0.12813 0.167
## siRNAsiBRCA1:genotypeYFP-ALC1 0.06495 0.02026 3.206
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.61639 0.18120 3.402
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.23463 0.18120 1.295
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 < 2e-16 ***
## poly(Olaparib, 2)2 1.23e-05 ***
## siRNAsiBRCA1 4.87e-10 ***
## genotypeYFP-ALC1 0.000103 ***
## poly(Olaparib, 2)1:siRNAsiBRCA1 4.34e-07 ***
## poly(Olaparib, 2)2:siRNAsiBRCA1 0.833222
## poly(Olaparib, 2)1:genotypeYFP-ALC1 0.010337 *
## poly(Olaparib, 2)2:genotypeYFP-ALC1 0.867528
## siRNAsiBRCA1:genotypeYFP-ALC1 0.002052 **
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.001125 **
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.199729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0453 on 68 degrees of freedom
## Multiple R-squared: 0.9618, Adjusted R-squared: 0.9556
## F-statistic: 155.5 on 11 and 68 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit7))
## AIC: -255.0861
simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * siRNA * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 1017.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.07780 -0.36639 -0.01297 0.34311 3.02814
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 945657 972.4
## Residual 71655 267.7
## Number of obs: 80, groups: UID, 20
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1791.20 438.99 16.00
## poly(Olaparib, 2)1 -3813.51 535.37 52.00
## poly(Olaparib, 2)2 -954.25 535.37 52.00
## siRNAsiBRCA1 -140.15 620.83 16.00
## genotypeYFP-ALC1 13.85 620.83 16.00
## poly(Olaparib, 2)1:siRNAsiBRCA1 -1947.25 757.13 52.00
## poly(Olaparib, 2)2:siRNAsiBRCA1 158.88 757.13 52.00
## poly(Olaparib, 2)1:genotypeYFP-ALC1 960.61 757.13 52.00
## poly(Olaparib, 2)2:genotypeYFP-ALC1 18.02 757.13 52.00
## siRNAsiBRCA1:genotypeYFP-ALC1 -391.70 877.98 16.00
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 2582.11 1070.74 52.00
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 476.17 1070.74 52.00
## t value Pr(>|t|)
## (Intercept) 4.080 0.000872 ***
## poly(Olaparib, 2)1 -7.123 3.15e-09 ***
## poly(Olaparib, 2)2 -1.782 0.080520 .
## siRNAsiBRCA1 -0.226 0.824257
## genotypeYFP-ALC1 0.022 0.982477
## poly(Olaparib, 2)1:siRNAsiBRCA1 -2.572 0.013012 *
## poly(Olaparib, 2)2:siRNAsiBRCA1 0.210 0.834608
## poly(Olaparib, 2)1:genotypeYFP-ALC1 1.269 0.210179
## poly(Olaparib, 2)2:genotypeYFP-ALC1 0.024 0.981108
## siRNAsiBRCA1:genotypeYFP-ALC1 -0.446 0.661478
## poly(Olaparib, 2)1:siRNAsiBRCA1:genotypeYFP-ALC1 2.412 0.019453 *
## poly(Olaparib, 2)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.445 0.658373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(O,2)1 pl(O,2)2 sRNAsBRCA1 gYFP-A pl(O,2)1:RNABRCA1
## ply(Olp,2)1 0.000
## ply(Olp,2)2 0.000 0.000
## siRNAsBRCA1 -0.707 0.000 0.000
## gntYFP-ALC1 -0.707 0.000 0.000 0.500
## pl(O,2)1:RNABRCA1 0.000 -0.707 0.000 0.000 0.000
## pl(O,2)2:RNABRCA1 0.000 0.000 -0.707 0.000 0.000 0.000
## p(O,2)1:YFP 0.000 -0.707 0.000 0.000 0.000 0.500
## p(O,2)2:YFP 0.000 0.000 -0.707 0.000 0.000 0.000
## sRNABRCA1:Y 0.500 0.000 0.000 -0.707 -0.707 0.000
## p(O,2)1:RNABRCA1: 0.000 0.500 0.000 0.000 0.000 -0.707
## p(O,2)2:RNABRCA1: 0.000 0.000 0.500 0.000 0.000 0.000
## pl(O,2)2:RNABRCA1 p(O,2)1:Y p(O,2)2:Y sRNABRCA1:
## ply(Olp,2)1
## ply(Olp,2)2
## siRNAsBRCA1
## gntYFP-ALC1
## pl(O,2)1:RNABRCA1
## pl(O,2)2:RNABRCA1
## p(O,2)1:YFP 0.000
## p(O,2)2:YFP 0.500 0.000
## sRNABRCA1:Y 0.000 0.000 0.000
## p(O,2)1:RNABRCA1: 0.000 -0.707 0.000 0.000
## p(O,2)2:RNABRCA1: -0.707 0.000 -0.707 0.000
## p(O,2)1:RNABRCA1:
## ply(Olp,2)1
## ply(Olp,2)2
## siRNAsBRCA1
## gntYFP-ALC1
## pl(O,2)1:RNABRCA1
## pl(O,2)2:RNABRCA1
## p(O,2)1:YFP
## p(O,2)2:YFP
## sRNABRCA1:Y
## p(O,2)1:RNABRCA1:
## p(O,2)2:RNABRCA1: 0.000
cat("AIC: ", AIC(fit8))
## AIC: 1045.844
simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula
fit9 <- lm(Counts ~ Experiment + poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit9))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * siRNA *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -932.95 -192.29 42.57 237.90 1206.05
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1657.51 140.33 11.811
## Experimentexp2 -68.56 156.90 -0.437
## Experimentexp3 1714.44 156.90 10.927
## Experimentexp4 -523.62 156.90 -3.337
## Experimentexp5 -453.81 156.90 -2.892
## poly(Olaparib, 3)1 -3813.51 887.54 -4.297
## poly(Olaparib, 3)2 -954.25 887.54 -1.075
## poly(Olaparib, 3)3 -235.30 887.54 -0.265
## siRNAsiBRCA1 -140.15 140.33 -0.999
## genotypeYFP-ALC1 13.85 140.33 0.099
## poly(Olaparib, 3)1:siRNAsiBRCA1 -1947.25 1255.17 -1.551
## poly(Olaparib, 3)2:siRNAsiBRCA1 158.88 1255.17 0.127
## poly(Olaparib, 3)3:siRNAsiBRCA1 936.68 1255.17 0.746
## poly(Olaparib, 3)1:genotypeYFP-ALC1 960.61 1255.17 0.765
## poly(Olaparib, 3)2:genotypeYFP-ALC1 18.02 1255.17 0.014
## poly(Olaparib, 3)3:genotypeYFP-ALC1 683.49 1255.17 0.545
## siRNAsiBRCA1:genotypeYFP-ALC1 -391.70 198.46 -1.974
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 2582.11 1775.08 1.455
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 476.17 1775.08 0.268
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -1008.92 1775.08 -0.568
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Experimentexp2 0.66369
## Experimentexp3 6.69e-16 ***
## Experimentexp4 0.00146 **
## Experimentexp5 0.00532 **
## poly(Olaparib, 3)1 6.45e-05 ***
## poly(Olaparib, 3)2 0.28661
## poly(Olaparib, 3)3 0.79183
## siRNAsiBRCA1 0.32195
## genotypeYFP-ALC1 0.92171
## poly(Olaparib, 3)1:siRNAsiBRCA1 0.12607
## poly(Olaparib, 3)2:siRNAsiBRCA1 0.89970
## poly(Olaparib, 3)3:siRNAsiBRCA1 0.45843
## poly(Olaparib, 3)1:genotypeYFP-ALC1 0.44708
## poly(Olaparib, 3)2:genotypeYFP-ALC1 0.98860
## poly(Olaparib, 3)3:genotypeYFP-ALC1 0.58809
## siRNAsiBRCA1:genotypeYFP-ALC1 0.05303 .
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.15098
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.78943
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 0.57190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 443.8 on 60 degrees of freedom
## Multiple R-squared: 0.8608, Adjusted R-squared: 0.8168
## F-statistic: 19.53 on 19 and 60 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit9))
## AIC: 1221.265
simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit10))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * siRNA * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.147953 -0.025716 -0.002438 0.029525 0.122120
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 1.113e-02 89.848
## poly(Olaparib, 3)1 -2.173e+00 9.955e-02 -21.827
## poly(Olaparib, 3)2 -5.550e-01 9.955e-02 -5.575
## poly(Olaparib, 3)3 -1.224e-01 9.955e-02 -1.229
## siRNAsiBRCA1 4.100e-16 1.574e-02 0.000
## genotypeYFP-ALC1 2.941e-16 1.574e-02 0.000
## poly(Olaparib, 3)1:siRNAsiBRCA1 -1.415e+00 1.408e-01 -10.049
## poly(Olaparib, 3)2:siRNAsiBRCA1 -4.597e-02 1.408e-01 -0.326
## poly(Olaparib, 3)3:siRNAsiBRCA1 6.700e-01 1.408e-01 4.759
## poly(Olaparib, 3)1:genotypeYFP-ALC1 5.625e-01 1.408e-01 3.996
## poly(Olaparib, 3)2:genotypeYFP-ALC1 6.544e-02 1.408e-01 0.465
## poly(Olaparib, 3)3:genotypeYFP-ALC1 2.601e-01 1.408e-01 1.847
## siRNAsiBRCA1:genotypeYFP-ALC1 -3.152e-16 2.226e-02 0.000
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 1.209e+00 1.991e-01 6.071
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 3.531e-01 1.991e-01 1.773
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -5.132e-01 1.991e-01 -2.578
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 < 2e-16 ***
## poly(Olaparib, 3)2 5.32e-07 ***
## poly(Olaparib, 3)3 0.223550
## siRNAsiBRCA1 1.000000
## genotypeYFP-ALC1 1.000000
## poly(Olaparib, 3)1:siRNAsiBRCA1 8.69e-15 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1 0.745112
## poly(Olaparib, 3)3:siRNAsiBRCA1 1.15e-05 ***
## poly(Olaparib, 3)1:genotypeYFP-ALC1 0.000169 ***
## poly(Olaparib, 3)2:genotypeYFP-ALC1 0.643648
## poly(Olaparib, 3)3:genotypeYFP-ALC1 0.069306 .
## siRNAsiBRCA1:genotypeYFP-ALC1 1.000000
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 7.66e-08 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.080940 .
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 0.012260 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04977 on 64 degrees of freedom
## Multiple R-squared: 0.9751, Adjusted R-squared: 0.9693
## F-statistic: 167.2 on 15 and 64 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit10))
## AIC: -236.8624
simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit11))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * siRNA * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.098539 -0.020350 -0.001746 0.021531 0.081334
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.769972 0.008257 93.251
## poly(Olaparib, 3)1 -1.673026 0.073853 -22.654
## poly(Olaparib, 3)2 -0.427341 0.073853 -5.786
## poly(Olaparib, 3)3 -0.094207 0.073853 -1.276
## siRNAsiBRCA1 -0.103957 0.011677 -8.903
## genotypeYFP-ALC1 0.059097 0.011677 5.061
## poly(Olaparib, 3)1:siRNAsiBRCA1 -0.716369 0.104443 -6.859
## poly(Olaparib, 3)2:siRNAsiBRCA1 0.027083 0.104443 0.259
## poly(Olaparib, 3)3:siRNAsiBRCA1 0.458933 0.104443 4.394
## poly(Olaparib, 3)1:genotypeYFP-ALC1 0.337956 0.104443 3.236
## poly(Olaparib, 3)2:genotypeYFP-ALC1 0.021452 0.104443 0.205
## poly(Olaparib, 3)3:genotypeYFP-ALC1 0.208401 0.104443 1.995
## siRNAsiBRCA1:genotypeYFP-ALC1 0.064947 0.016514 3.933
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 0.616391 0.147705 4.173
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.234634 0.147705 1.589
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -0.340450 0.147705 -2.305
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 < 2e-16 ***
## poly(Olaparib, 3)2 2.34e-07 ***
## poly(Olaparib, 3)3 0.206705
## siRNAsiBRCA1 8.37e-13 ***
## genotypeYFP-ALC1 3.77e-06 ***
## poly(Olaparib, 3)1:siRNAsiBRCA1 3.29e-09 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1 0.796227
## poly(Olaparib, 3)3:siRNAsiBRCA1 4.27e-05 ***
## poly(Olaparib, 3)1:genotypeYFP-ALC1 0.001922 **
## poly(Olaparib, 3)2:genotypeYFP-ALC1 0.837915
## poly(Olaparib, 3)3:genotypeYFP-ALC1 0.050266 .
## siRNAsiBRCA1:genotypeYFP-ALC1 0.000209 ***
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 9.24e-05 ***
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.117095
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 0.024424 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03693 on 64 degrees of freedom
## Multiple R-squared: 0.9761, Adjusted R-squared: 0.9705
## F-statistic: 174.2 on 15 and 64 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit11))
## AIC: -284.6344
simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * siRNA * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 957.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20787 -0.42360 -0.02922 0.33628 3.08502
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 945322 972.3
## Residual 72994 270.2
## Number of obs: 80, groups: UID, 20
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1791.20 438.99 16.00
## poly(Olaparib, 3)1 -3813.51 540.35 48.00
## poly(Olaparib, 3)2 -954.25 540.35 48.00
## poly(Olaparib, 3)3 -235.30 540.35 48.00
## siRNAsiBRCA1 -140.15 620.83 16.00
## genotypeYFP-ALC1 13.85 620.83 16.00
## poly(Olaparib, 3)1:siRNAsiBRCA1 -1947.25 764.17 48.00
## poly(Olaparib, 3)2:siRNAsiBRCA1 158.88 764.17 48.00
## poly(Olaparib, 3)3:siRNAsiBRCA1 936.68 764.17 48.00
## poly(Olaparib, 3)1:genotypeYFP-ALC1 960.61 764.17 48.00
## poly(Olaparib, 3)2:genotypeYFP-ALC1 18.02 764.17 48.00
## poly(Olaparib, 3)3:genotypeYFP-ALC1 683.49 764.17 48.00
## siRNAsiBRCA1:genotypeYFP-ALC1 -391.70 877.98 16.00
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 2582.11 1080.69 48.00
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 476.17 1080.69 48.00
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -1008.92 1080.69 48.00
## t value Pr(>|t|)
## (Intercept) 4.080 0.000872 ***
## poly(Olaparib, 3)1 -7.058 6e-09 ***
## poly(Olaparib, 3)2 -1.766 0.083756 .
## poly(Olaparib, 3)3 -0.435 0.665177
## siRNAsiBRCA1 -0.226 0.824257
## genotypeYFP-ALC1 0.022 0.982477
## poly(Olaparib, 3)1:siRNAsiBRCA1 -2.548 0.014083 *
## poly(Olaparib, 3)2:siRNAsiBRCA1 0.208 0.836177
## poly(Olaparib, 3)3:siRNAsiBRCA1 1.226 0.226271
## poly(Olaparib, 3)1:genotypeYFP-ALC1 1.257 0.214811
## poly(Olaparib, 3)2:genotypeYFP-ALC1 0.024 0.981289
## poly(Olaparib, 3)3:genotypeYFP-ALC1 0.894 0.375558
## siRNAsiBRCA1:genotypeYFP-ALC1 -0.446 0.661478
## poly(Olaparib, 3)1:siRNAsiBRCA1:genotypeYFP-ALC1 2.389 0.020858 *
## poly(Olaparib, 3)2:siRNAsiBRCA1:genotypeYFP-ALC1 0.441 0.661469
## poly(Olaparib, 3)3:siRNAsiBRCA1:genotypeYFP-ALC1 -0.934 0.355191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit12))
## AIC: 993.1168
simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Final Result
fit <- fit11
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
rownames(output) <- gsub("siRNA", paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$siRNA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in WT siCtrl |
-1.6730264 |
0.0738526 |
-22.6536025 |
0.0000000 |
| Olaparib2 in WT siCtrl |
-0.4273414 |
0.0738526 |
-5.7864132 |
0.0000002 |
| Olaparib3 in WT siCtrl |
-0.0942069 |
0.0738526 |
-1.2756078 |
0.2067048 |
| Olaparib1: siCtrl vs. siBRCA1 in WT |
-0.7163693 |
0.1044433 |
-6.8589315 |
0.0000000 |
| Olaparib2: siCtrl vs. siBRCA1 in WT |
0.0270834 |
0.1044433 |
0.2593116 |
0.7962271 |
| Olaparib3: siCtrl vs. siBRCA1 in WT |
0.4589328 |
0.1044433 |
4.3940864 |
0.0000427 |
| Olaparib1: WT vs. YFP-ALC1 in siCtrl |
0.3379562 |
0.1044433 |
3.2357872 |
0.0019220 |
| Olaparib2: WT vs. YFP-ALC1 in siCtrl |
0.0214522 |
0.1044433 |
0.2053957 |
0.8379149 |
| Olaparib3: WT vs. YFP-ALC1 in siCtrl |
0.2084006 |
0.1044433 |
1.9953475 |
0.0502657 |
| Olaparib1: siCtrl vs. siBRCA1: WT vs. YFP-ALC1 |
0.6163909 |
0.1477051 |
4.1731182 |
0.0000924 |
| Olaparib2: siCtrl vs. siBRCA1: WT vs. YFP-ALC1 |
0.2346342 |
0.1477051 |
1.5885316 |
0.1170946 |
| Olaparib3: siCtrl vs. siBRCA1: WT vs. YFP-ALC1 |
-0.3404500 |
0.1477051 |
-2.3049303 |
0.0244245 |
write.table(output, file = "Figure5G_Stats_Ref_WT_siCtrl.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with siBRCA1 reference
dataset$siRNA <- relevel(dataset$siRNA, ref = "siBRCA1")
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
rownames(output) <- gsub("siRNA", paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$siRNA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in WT siBRCA1 |
-2.3893957 |
0.0738526 |
-32.3535964 |
0.0000000 |
| Olaparib2 in WT siBRCA1 |
-0.4002580 |
0.0738526 |
-5.4196913 |
0.0000010 |
| Olaparib3 in WT siBRCA1 |
0.3647259 |
0.0738526 |
4.9385688 |
0.0000059 |
| Olaparib1: siBRCA1 vs. siCtrl in WT |
0.7163693 |
0.1044433 |
6.8589315 |
0.0000000 |
| Olaparib2: siBRCA1 vs. siCtrl in WT |
-0.0270834 |
0.1044433 |
-0.2593116 |
0.7962271 |
| Olaparib3: siBRCA1 vs. siCtrl in WT |
-0.4589328 |
0.1044433 |
-4.3940864 |
0.0000427 |
| Olaparib1: WT vs. YFP-ALC1 in siBRCA1 |
0.9543471 |
0.1044433 |
9.1374676 |
0.0000000 |
| Olaparib2: WT vs. YFP-ALC1 in siBRCA1 |
0.2560864 |
0.1044433 |
2.4519186 |
0.0169487 |
| Olaparib3: WT vs. YFP-ALC1 in siBRCA1 |
-0.1320493 |
0.1044433 |
-1.2643162 |
0.2107017 |
| Olaparib1: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1 |
-0.6163909 |
0.1477051 |
-4.1731182 |
0.0000924 |
| Olaparib2: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1 |
-0.2346342 |
0.1477051 |
-1.5885316 |
0.1170946 |
| Olaparib3: siBRCA1 vs. siCtrl: WT vs. YFP-ALC1 |
0.3404500 |
0.1477051 |
2.3049303 |
0.0244245 |
write.table(output, file = "Figure5G_Stats_Ref_WT_siBRCA1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)